Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations53000
Missing cells14000
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 MiB
Average record size in memory303.0 B

Variable types

Numeric11
Text1
Categorical3

Alerts

model_type has constant value "CPF" Constant
request_size has constant value "16KB" Constant
Acquisition_Score is highly overall correlated with DB_Memory and 2 other fieldsHigh correlation
DB_Memory is highly overall correlated with Acquisition_Score and 2 other fieldsHigh correlation
MAS_Workers is highly overall correlated with Selection_Method and 2 other fieldsHigh correlation
Parallel_Jobs is highly overall correlated with par_normHigh correlation
Selection_Method is highly overall correlated with Acquisition_Score and 6 other fieldsHigh correlation
db_norm is highly overall correlated with Acquisition_Score and 2 other fieldsHigh correlation
execution_time_full is highly overall correlated with MAS_Workers and 2 other fieldsHigh correlation
execution_time_some is highly overall correlated with Selection_MethodHigh correlation
mas_norm is highly overall correlated with MAS_Workers and 2 other fieldsHigh correlation
par_norm is highly overall correlated with Parallel_JobsHigh correlation
Selection_Method has 1000 (1.9%) missing values Missing
Acquisition_Score has 13000 (24.5%) missing values Missing
Execution_Time has unique values Unique
db_norm has 1000 (1.9%) zeros Zeros
mas_norm has 19000 (35.8%) zeros Zeros
par_norm has 7000 (13.2%) zeros Zeros

Reproduction

Analysis started2025-06-10 14:20:42.250630
Analysis finished2025-06-10 14:21:04.795555
Duration22.54 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

DB_Memory
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1141.5094
Minimum250
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:04.916812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile659.09091
Q1863.63636
median1068.1818
Q31477.2727
95-th percentile2090.9091
Maximum2500
Range2250
Interquartile range (IQR)613.63636

Descriptive statistics

Standard deviation428.13084
Coefficient of variation (CV)0.37505677
Kurtosis1.2795973
Mean1141.5094
Median Absolute Deviation (MAD)204.54545
Skewness1.001798
Sum60500000
Variance183296.02
MonotonicityNot monotonic
2025-06-10T16:21:05.038440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
863.6363636 22000
41.5%
1477.272727 11000
20.8%
1068.181818 6000
 
11.3%
1272.727273 5000
 
9.4%
659.0909091 2000
 
3.8%
250 1000
 
1.9%
2500 1000
 
1.9%
2295.454545 1000
 
1.9%
1681.818182 1000
 
1.9%
454.5454545 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
250 1000
 
1.9%
454.5454545 1000
 
1.9%
659.0909091 2000
 
3.8%
863.6363636 22000
41.5%
1068.181818 6000
 
11.3%
1272.727273 5000
 
9.4%
1477.272727 11000
20.8%
1681.818182 1000
 
1.9%
1886.363636 1000
 
1.9%
2090.909091 1000
 
1.9%
ValueCountFrequency (%)
2500 1000
 
1.9%
2295.454545 1000
 
1.9%
2090.909091 1000
 
1.9%
1886.363636 1000
 
1.9%
1681.818182 1000
 
1.9%
1477.272727 11000
20.8%
1272.727273 5000
 
9.4%
1068.181818 6000
 
11.3%
863.6363636 22000
41.5%
659.0909091 2000
 
3.8%

MAS_Workers
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.926244
Minimum10
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:05.175407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median16.363636
Q316.363636
95-th percentile67.272727
Maximum80
Range70
Interquartile range (IQR)6.3636364

Descriptive statistics

Standard deviation17.056667
Coefficient of variation (CV)0.81508497
Kurtosis3.5591735
Mean20.926244
Median Absolute Deviation (MAD)6.3636364
Skewness2.1317592
Sum1109090.9
Variance290.92988
MonotonicityNot monotonic
2025-06-10T16:21:05.321802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
16.36363636 23000
43.4%
10 19000
35.8%
29.09090909 2000
 
3.8%
54.54545455 1000
 
1.9%
67.27272727 1000
 
1.9%
22.72727273 1000
 
1.9%
60.90909091 1000
 
1.9%
48.18181818 1000
 
1.9%
80 1000
 
1.9%
73.63636364 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
10 19000
35.8%
16.36363636 23000
43.4%
22.72727273 1000
 
1.9%
29.09090909 2000
 
3.8%
35.45454545 1000
 
1.9%
41.81818182 1000
 
1.9%
48.18181818 1000
 
1.9%
54.54545455 1000
 
1.9%
60.90909091 1000
 
1.9%
67.27272727 1000
 
1.9%
ValueCountFrequency (%)
80 1000
1.9%
73.63636364 1000
1.9%
67.27272727 1000
1.9%
60.90909091 1000
1.9%
54.54545455 1000
1.9%
48.18181818 1000
1.9%
41.81818182 1000
1.9%
35.45454545 1000
1.9%
29.09090909 2000
3.8%
22.72727273 1000
1.9%

Parallel_Jobs
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.348199
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:05.472466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13.4545455
median4.9090909
Q37.0909091
95-th percentile9.2727273
Maximum10
Range8
Interquartile range (IQR)3.6363636

Descriptive statistics

Standard deviation2.361265
Coefficient of variation (CV)0.44150658
Kurtosis-0.99508033
Mean5.348199
Median Absolute Deviation (MAD)2.1818182
Skewness0.24059054
Sum283454.55
Variance5.5755725
MonotonicityNot monotonic
2025-06-10T16:21:05.623660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 7000
13.2%
4.181818182 6000
11.3%
4.909090909 5000
9.4%
6.363636364 5000
9.4%
5.636363636 5000
9.4%
2.727272727 5000
9.4%
7.090909091 5000
9.4%
3.454545455 4000
7.5%
7.818181818 3000
5.7%
9.272727273 3000
5.7%
Other values (2) 5000
9.4%
ValueCountFrequency (%)
2 7000
13.2%
2.727272727 5000
9.4%
3.454545455 4000
7.5%
4.181818182 6000
11.3%
4.909090909 5000
9.4%
5.636363636 5000
9.4%
6.363636364 5000
9.4%
7.090909091 5000
9.4%
7.818181818 3000
5.7%
8.545454545 3000
5.7%
ValueCountFrequency (%)
10 2000
 
3.8%
9.272727273 3000
5.7%
8.545454545 3000
5.7%
7.818181818 3000
5.7%
7.090909091 5000
9.4%
6.363636364 5000
9.4%
5.636363636 5000
9.4%
4.909090909 5000
9.4%
4.181818182 6000
11.3%
3.454545455 4000
7.5%
Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2025-06-10T16:21:06.115547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.09434
Min length10

Characters and Unicode

Total characters535000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConfig_665
2nd rowConfig_665
3rd rowConfig_665
4th rowConfig_665
5th rowConfig_665
ValueCountFrequency (%)
config_665 1000
 
1.9%
config_480 1000
 
1.9%
config_111 1000
 
1.9%
config_1609 1000
 
1.9%
config_1545 1000
 
1.9%
config_307 1000
 
1.9%
config_1014 1000
 
1.9%
config_227 1000
 
1.9%
config_862 1000
 
1.9%
config_1418 1000
 
1.9%
Other values (43) 43000
81.1%
2025-06-10T16:21:06.764123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 53000
9.9%
n 53000
9.9%
f 53000
9.9%
i 53000
9.9%
g 53000
9.9%
_ 53000
9.9%
o 53000
9.9%
4 42000
7.9%
8 24000
 
4.5%
3 16000
 
3.0%
Other values (7) 82000
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 535000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 53000
9.9%
n 53000
9.9%
f 53000
9.9%
i 53000
9.9%
g 53000
9.9%
_ 53000
9.9%
o 53000
9.9%
4 42000
7.9%
8 24000
 
4.5%
3 16000
 
3.0%
Other values (7) 82000
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 535000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 53000
9.9%
n 53000
9.9%
f 53000
9.9%
i 53000
9.9%
g 53000
9.9%
_ 53000
9.9%
o 53000
9.9%
4 42000
7.9%
8 24000
 
4.5%
3 16000
 
3.0%
Other values (7) 82000
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 535000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 53000
9.9%
n 53000
9.9%
f 53000
9.9%
i 53000
9.9%
g 53000
9.9%
_ 53000
9.9%
o 53000
9.9%
4 42000
7.9%
8 24000
 
4.5%
3 16000
 
3.0%
Other values (7) 82000
15.3%

model_type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
CPF
53000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCPF
2nd rowCPF
3rd rowCPF
4th rowCPF
5th rowCPF

Common Values

ValueCountFrequency (%)
CPF 53000
100.0%

Length

2025-06-10T16:21:06.883784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-10T16:21:06.971555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cpf 53000
100.0%

Most occurring characters

ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 53000
33.3%
P 53000
33.3%
F 53000
33.3%

request_size
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
16KB
53000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters212000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16KB
2nd row16KB
3rd row16KB
4th row16KB
5th row16KB

Common Values

ValueCountFrequency (%)
16KB 53000
100.0%

Length

2025-06-10T16:21:07.066758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-10T16:21:07.144428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16kb 53000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 53000
25.0%
6 53000
25.0%
K 53000
25.0%
B 53000
25.0%

db_norm
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39622642
Minimum0
Maximum1
Zeros1000
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:07.224674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18181818
Q10.27272727
median0.36363636
Q30.54545455
95-th percentile0.81818182
Maximum1
Range1
Interquartile range (IQR)0.27272727

Descriptive statistics

Standard deviation0.19028037
Coefficient of variation (CV)0.48023142
Kurtosis1.2795973
Mean0.39622642
Median Absolute Deviation (MAD)0.090909091
Skewness1.001798
Sum21000
Variance0.036206621
MonotonicityNot monotonic
2025-06-10T16:21:07.355536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.2727272727 22000
41.5%
0.5454545455 11000
20.8%
0.3636363636 6000
 
11.3%
0.4545454545 5000
 
9.4%
0.1818181818 2000
 
3.8%
0 1000
 
1.9%
1 1000
 
1.9%
0.9090909091 1000
 
1.9%
0.6363636364 1000
 
1.9%
0.09090909091 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
0 1000
 
1.9%
0.09090909091 1000
 
1.9%
0.1818181818 2000
 
3.8%
0.2727272727 22000
41.5%
0.3636363636 6000
 
11.3%
0.4545454545 5000
 
9.4%
0.5454545455 11000
20.8%
0.6363636364 1000
 
1.9%
0.7272727273 1000
 
1.9%
0.8181818182 1000
 
1.9%
ValueCountFrequency (%)
1 1000
 
1.9%
0.9090909091 1000
 
1.9%
0.8181818182 1000
 
1.9%
0.7272727273 1000
 
1.9%
0.6363636364 1000
 
1.9%
0.5454545455 11000
20.8%
0.4545454545 5000
 
9.4%
0.3636363636 6000
 
11.3%
0.2727272727 22000
41.5%
0.1818181818 2000
 
3.8%

mas_norm
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15608919
Minimum0
Maximum1
Zeros19000
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:07.513591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.090909091
Q30.090909091
95-th percentile0.81818182
Maximum1
Range1
Interquartile range (IQR)0.090909091

Descriptive statistics

Standard deviation0.24366667
Coefficient of variation (CV)1.5610733
Kurtosis3.5591735
Mean0.15608919
Median Absolute Deviation (MAD)0.090909091
Skewness2.1317592
Sum8272.7273
Variance0.059373445
MonotonicityNot monotonic
2025-06-10T16:21:07.640882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.09090909091 23000
43.4%
0 19000
35.8%
0.2727272727 2000
 
3.8%
0.6363636364 1000
 
1.9%
0.8181818182 1000
 
1.9%
0.1818181818 1000
 
1.9%
0.7272727273 1000
 
1.9%
0.5454545455 1000
 
1.9%
1 1000
 
1.9%
0.9090909091 1000
 
1.9%
Other values (2) 2000
 
3.8%
ValueCountFrequency (%)
0 19000
35.8%
0.09090909091 23000
43.4%
0.1818181818 1000
 
1.9%
0.2727272727 2000
 
3.8%
0.3636363636 1000
 
1.9%
0.4545454545 1000
 
1.9%
0.5454545455 1000
 
1.9%
0.6363636364 1000
 
1.9%
0.7272727273 1000
 
1.9%
0.8181818182 1000
 
1.9%
ValueCountFrequency (%)
1 1000
1.9%
0.9090909091 1000
1.9%
0.8181818182 1000
1.9%
0.7272727273 1000
1.9%
0.6363636364 1000
1.9%
0.5454545455 1000
1.9%
0.4545454545 1000
1.9%
0.3636363636 1000
1.9%
0.2727272727 2000
3.8%
0.1818181818 1000
1.9%

par_norm
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41852487
Minimum0
Maximum1
Zeros7000
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:07.759132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.18181818
median0.36363636
Q30.63636364
95-th percentile0.90909091
Maximum1
Range1
Interquartile range (IQR)0.45454545

Descriptive statistics

Standard deviation0.29515813
Coefficient of variation (CV)0.70523438
Kurtosis-0.99508033
Mean0.41852487
Median Absolute Deviation (MAD)0.27272727
Skewness0.24059054
Sum22181.818
Variance0.08711832
MonotonicityNot monotonic
2025-06-10T16:21:07.905067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 7000
13.2%
0.2727272727 6000
11.3%
0.3636363636 5000
9.4%
0.5454545455 5000
9.4%
0.4545454545 5000
9.4%
0.09090909091 5000
9.4%
0.6363636364 5000
9.4%
0.1818181818 4000
7.5%
0.7272727273 3000
5.7%
0.9090909091 3000
5.7%
Other values (2) 5000
9.4%
ValueCountFrequency (%)
0 7000
13.2%
0.09090909091 5000
9.4%
0.1818181818 4000
7.5%
0.2727272727 6000
11.3%
0.3636363636 5000
9.4%
0.4545454545 5000
9.4%
0.5454545455 5000
9.4%
0.6363636364 5000
9.4%
0.7272727273 3000
5.7%
0.8181818182 3000
5.7%
ValueCountFrequency (%)
1 2000
 
3.8%
0.9090909091 3000
5.7%
0.8181818182 3000
5.7%
0.7272727273 3000
5.7%
0.6363636364 5000
9.4%
0.5454545455 5000
9.4%
0.4545454545 5000
9.4%
0.3636363636 5000
9.4%
0.2727272727 6000
11.3%
0.1818181818 4000
7.5%

execution_time_none
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.493318
Minimum41.562192
Maximum64.295534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:08.116029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum41.562192
5-th percentile42.051335
Q145.760511
median50.59332
Q353.942099
95-th percentile58.554129
Maximum64.295534
Range22.733342
Interquartile range (IQR)8.1815878

Descriptive statistics

Standard deviation5.3809264
Coefficient of variation (CV)0.1065671
Kurtosis-0.66171071
Mean50.493318
Median Absolute Deviation (MAD)3.8698354
Skewness0.26595944
Sum2676145.8
Variance28.954369
MonotonicityNot monotonic
2025-06-10T16:21:08.301867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.25226869 1000
 
1.9%
49.59224999 1000
 
1.9%
45.6494518 1000
 
1.9%
57.21378221 1000
 
1.9%
52.06064369 1000
 
1.9%
44.21724366 1000
 
1.9%
43.19531748 1000
 
1.9%
45.57644713 1000
 
1.9%
53.80269987 1000
 
1.9%
54.4452472 1000
 
1.9%
Other values (43) 43000
81.1%
ValueCountFrequency (%)
41.56219166 1000
1.9%
41.97844363 1000
1.9%
42.05133453 1000
1.9%
42.67517083 1000
1.9%
43.19531748 1000
1.9%
43.80575725 1000
1.9%
44.13104309 1000
1.9%
44.18238309 1000
1.9%
44.21724366 1000
1.9%
44.39358426 1000
1.9%
ValueCountFrequency (%)
64.29553399 1000
1.9%
60.35363982 1000
1.9%
58.55412927 1000
1.9%
58.53153185 1000
1.9%
58.48806375 1000
1.9%
57.89681003 1000
1.9%
57.43482545 1000
1.9%
57.21378221 1000
1.9%
56.47185089 1000
1.9%
55.85859423 1000
1.9%

execution_time_some
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.750375
Minimum50.859836
Maximum71.246124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:08.527341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.859836
5-th percentile57.162309
Q162.273067
median64.369275
Q366.029333
95-th percentile68.666535
Maximum71.246124
Range20.386288
Interquartile range (IQR)3.756266

Descriptive statistics

Standard deviation3.5236708
Coefficient of variation (CV)0.055272942
Kurtosis2.1982535
Mean63.750375
Median Absolute Deviation (MAD)1.9486822
Skewness-0.99626466
Sum3378769.9
Variance12.416256
MonotonicityNot monotonic
2025-06-10T16:21:08.736809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.7471524 1000
 
1.9%
64.67892259 1000
 
1.9%
68.26817416 1000
 
1.9%
61.98687118 1000
 
1.9%
64.01387336 1000
 
1.9%
64.9305016 1000
 
1.9%
65.21688887 1000
 
1.9%
63.66281861 1000
 
1.9%
65.01278769 1000
 
1.9%
62.27922582 1000
 
1.9%
Other values (43) 43000
81.1%
ValueCountFrequency (%)
50.85983606 1000
1.9%
56.78941718 1000
1.9%
57.16230917 1000
1.9%
57.17200638 1000
1.9%
57.7471524 1000
1.9%
59.04727778 1000
1.9%
60.53424577 1000
1.9%
60.61992901 1000
1.9%
61.25773864 1000
1.9%
61.33319905 1000
1.9%
ValueCountFrequency (%)
71.246124 1000
1.9%
69.82257328 1000
1.9%
68.6665346 1000
1.9%
68.56436598 1000
1.9%
68.26817416 1000
1.9%
67.30825228 1000
1.9%
66.71784874 1000
1.9%
66.56334132 1000
1.9%
66.51919776 1000
1.9%
66.3179572 1000
1.9%

execution_time_full
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.94984
Minimum74.691011
Maximum123.59152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:08.969730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum74.691011
5-th percentile86.979066
Q1103.24405
median107.98772
Q3113.72238
95-th percentile119.23304
Maximum123.59152
Range48.900511
Interquartile range (IQR)10.478324

Descriptive statistics

Standard deviation9.0048562
Coefficient of variation (CV)0.084197005
Kurtosis1.9443843
Mean106.94984
Median Absolute Deviation (MAD)5.4997611
Skewness-1.0739629
Sum5668341.5
Variance81.087436
MonotonicityNot monotonic
2025-06-10T16:21:09.200129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.13393617 1000
 
1.9%
119.2330414 1000
 
1.9%
115.1632325 1000
 
1.9%
102.4328553 1000
 
1.9%
102.7788344 1000
 
1.9%
105.6406008 1000
 
1.9%
110.7706952 1000
 
1.9%
109.7405177 1000
 
1.9%
110.0546279 1000
 
1.9%
103.2440536 1000
 
1.9%
Other values (43) 43000
81.1%
ValueCountFrequency (%)
74.69101131 1000
1.9%
86.26865881 1000
1.9%
86.97906613 1000
1.9%
93.04402895 1000
1.9%
93.79426176 1000
1.9%
97.14272625 1000
1.9%
99.11974976 1000
1.9%
99.13393617 1000
1.9%
99.42260393 1000
1.9%
100.3695271 1000
1.9%
ValueCountFrequency (%)
123.5915223 1000
1.9%
121.5173466 1000
1.9%
119.2330414 1000
1.9%
116.6433596 1000
1.9%
116.5638406 1000
1.9%
116.4529709 1000
1.9%
115.5245435 1000
1.9%
115.5065831 1000
1.9%
115.1632325 1000
1.9%
114.8086215 1000
1.9%

Selection_Method
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1000
Missing (%)1.9%
Memory size2.6 MiB
BO
40000 
LHS
12000 

Length

Max length3
Median length2
Mean length2.2307692
Min length2

Characters and Unicode

Total characters116000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLHS
2nd rowLHS
3rd rowLHS
4th rowLHS
5th rowLHS

Common Values

ValueCountFrequency (%)
BO 40000
75.5%
LHS 12000
 
22.6%
(Missing) 1000
 
1.9%

Length

2025-06-10T16:21:09.401032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-10T16:21:09.493420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bo 40000
76.9%
lhs 12000
 
23.1%

Most occurring characters

ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 40000
34.5%
O 40000
34.5%
L 12000
 
10.3%
H 12000
 
10.3%
S 12000
 
10.3%

Execution_Time
Real number (ℝ)

Unique 

Distinct53000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.000637
Minimum41.370058
Maximum57.75092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:09.614520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum41.370058
5-th percentile46.673952
Q148.648623
median49.998707
Q351.356955
95-th percentile53.310465
Maximum57.75092
Range16.380861
Interquartile range (IQR)2.708332

Descriptive statistics

Standard deviation2.014164
Coefficient of variation (CV)0.040282767
Kurtosis0.010765539
Mean50.000637
Median Absolute Deviation (MAD)1.3544627
Skewness-0.010428952
Sum2650033.8
Variance4.0568567
MonotonicityNot monotonic
2025-06-10T16:21:09.790480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.48582827 1
 
< 0.1%
47.12871562 1
 
< 0.1%
49.22568109 1
 
< 0.1%
50.67874431 1
 
< 0.1%
50.89837336 1
 
< 0.1%
51.80487856 1
 
< 0.1%
54.83414341 1
 
< 0.1%
49.99420798 1
 
< 0.1%
49.32673797 1
 
< 0.1%
51.21031138 1
 
< 0.1%
Other values (52990) 52990
> 99.9%
ValueCountFrequency (%)
41.37005849 1
< 0.1%
41.57019502 1
< 0.1%
41.58979172 1
< 0.1%
41.78319139 1
< 0.1%
41.92462295 1
< 0.1%
42.06515202 1
< 0.1%
42.09706273 1
< 0.1%
42.26051752 1
< 0.1%
42.26269685 1
< 0.1%
42.53197864 1
< 0.1%
ValueCountFrequency (%)
57.75091976 1
< 0.1%
57.72169833 1
< 0.1%
57.47249573 1
< 0.1%
57.47156597 1
< 0.1%
57.36389291 1
< 0.1%
57.1959798 1
< 0.1%
57.14184481 1
< 0.1%
56.90397172 1
< 0.1%
56.85061363 1
< 0.1%
56.83305442 1
< 0.1%

Acquisition_Score
Real number (ℝ)

High correlation  Missing 

Distinct40
Distinct (%)0.1%
Missing13000
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean8.432881
Minimum8.030308
Maximum8.8558344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2025-06-10T16:21:09.954909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.030308
5-th percentile8.03548
Q18.2881961
median8.4109045
Q38.6070946
95-th percentile8.7537823
Maximum8.8558344
Range0.82552633
Interquartile range (IQR)0.31889851

Descriptive statistics

Standard deviation0.22113167
Coefficient of variation (CV)0.026222553
Kurtosis-0.84181709
Mean8.432881
Median Absolute Deviation (MAD)0.15807114
Skewness-0.1076298
Sum337315.24
Variance0.048899214
MonotonicityNot monotonic
2025-06-10T16:21:10.106884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
8.678316518 1000
 
1.9%
8.316610999 1000
 
1.9%
8.393176562 1000
 
1.9%
8.366356927 1000
 
1.9%
8.796895857 1000
 
1.9%
8.04386765 1000
 
1.9%
8.515685887 1000
 
1.9%
8.549195376 1000
 
1.9%
8.159993767 1000
 
1.9%
8.579487847 1000
 
1.9%
Other values (30) 30000
56.6%
(Missing) 13000
24.5%
ValueCountFrequency (%)
8.030308031 1000
1.9%
8.035395959 1000
1.9%
8.035484381 1000
1.9%
8.04386765 1000
1.9%
8.148703956 1000
1.9%
8.159993767 1000
1.9%
8.244675699 1000
1.9%
8.255386707 1000
1.9%
8.255492241 1000
1.9%
8.272702155 1000
1.9%
ValueCountFrequency (%)
8.855834366 1000
1.9%
8.796895857 1000
1.9%
8.751513178 1000
1.9%
8.747178409 1000
1.9%
8.706651114 1000
1.9%
8.692483516 1000
1.9%
8.678316518 1000
1.9%
8.64178757 1000
1.9%
8.620142124 1000
1.9%
8.617680382 1000
1.9%

Interactions

2025-06-10T16:21:02.172716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:43.900552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:45.517536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:47.132427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:49.265595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:50.969020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:52.626013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:54.298919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:55.833756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:57.468903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:59.053797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:02.356654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:44.039465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:45.658337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:47.265978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:49.411906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:51.118336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:52.765995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:54.443561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:55.970664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:57.593076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:59.228592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:02.537112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:44.192357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:45.800704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:47.913301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:49.552966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:51.292469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:52.918173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:54.579664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:56.121401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:57.732145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:59.425674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:02.727626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:44.336719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:45.971642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:48.058082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:49.699615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:51.477098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:53.078890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:54.720226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:56.286647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:57.873639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:00.656976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:02.886229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:44.493176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:46.126686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:48.185245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:49.860939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:51.642585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:53.233412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:54.847943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:56.431662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:58.001043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:00.859559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:03.024504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:44.631213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:46.283912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:48.322467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:50.021309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:51.809119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:53.382209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:54.972204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:56.582800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:58.140928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:01.116127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:03.222395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:44.797049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:46.425210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:48.455670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:50.170686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:51.944232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:53.506162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:55.113295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:56.741317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:58.288488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:01.317097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:03.438190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:44.930551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:46.575495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:48.616371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:50.336458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:52.070316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:53.642403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:55.244415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:56.882872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:58.410060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:01.480311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:03.608004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:45.089398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:46.720162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:48.796074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:50.514935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:52.212994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:53.820144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:55.400020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:57.032154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:58.557861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:01.677943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:03.775636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:45.225202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:46.863769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:48.956324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:50.663067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:52.350665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:53.968885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:55.541532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:57.184275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:58.694766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:01.850414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:03.923103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:45.375468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:47.004772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:49.116699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:50.823666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:52.494203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:54.137360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:55.688365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:57.341760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:20:58.883939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-10T16:21:02.001901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-10T16:21:10.237007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Acquisition_ScoreDB_MemoryExecution_TimeMAS_WorkersParallel_JobsSelection_Methoddb_normexecution_time_fullexecution_time_noneexecution_time_somemas_normpar_norm
Acquisition_Score1.000-0.615-0.001-0.2700.0461.000-0.615-0.189-0.051-0.238-0.2700.046
DB_Memory-0.6151.0000.0050.152-0.1300.7881.0000.160-0.2550.2780.152-0.130
Execution_Time-0.0010.0051.0000.0010.0000.0100.0050.002-0.0070.0070.0010.000
MAS_Workers-0.2700.1520.0011.0000.0470.8910.152-0.5070.168-0.2651.0000.047
Parallel_Jobs0.046-0.1300.0000.0471.0000.174-0.130-0.254-0.1640.3890.0471.000
Selection_Method1.0000.7880.0100.8910.1741.0000.7880.6740.4570.6480.8910.174
db_norm-0.6151.0000.0050.152-0.1300.7881.0000.160-0.2550.2780.152-0.130
execution_time_full-0.1890.1600.002-0.507-0.2540.6740.1601.000-0.1560.291-0.507-0.254
execution_time_none-0.051-0.255-0.0070.168-0.1640.457-0.255-0.1561.000-0.2580.168-0.164
execution_time_some-0.2380.2780.007-0.2650.3890.6480.2780.291-0.2581.000-0.2650.389
mas_norm-0.2700.1520.0011.0000.0470.8910.152-0.5070.168-0.2651.0000.047
par_norm0.046-0.1300.0000.0471.0000.174-0.130-0.254-0.1640.3890.0471.000

Missing values

2025-06-10T16:21:04.134897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-10T16:21:04.391981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-10T16:21:04.684516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DB_MemoryMAS_WorkersParallel_JobsConfigurationmodel_typerequest_sizedb_normmas_normpar_normexecution_time_noneexecution_time_someexecution_time_fullSelection_MethodExecution_TimeAcquisition_Score
01068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS49.485828NaN
11068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS46.757700NaN
21068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS50.997846NaN
31068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS46.598339NaN
41068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS51.325496NaN
51068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS48.900564NaN
61068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS47.831864NaN
71068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS49.156959NaN
81068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS50.538800NaN
91068.18181854.5454554.909091Config_665CPF16KB0.3636360.6363640.36363651.25226957.74715299.133936LHS48.004394NaN
DB_MemoryMAS_WorkersParallel_JobsConfigurationmodel_typerequest_sizedb_normmas_normpar_normexecution_time_noneexecution_time_someexecution_time_fullSelection_MethodExecution_TimeAcquisition_Score
52990659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN47.870324NaN
52991659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN45.043334NaN
52992659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN52.189648NaN
52993659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN50.160963NaN
52994659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN51.855195NaN
52995659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN53.504539NaN
52996659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN51.881931NaN
52997659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN48.976229NaN
52998659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN48.498289NaN
52999659.09090929.0909094.181818Config_328CPF16KB0.1818180.2727270.27272753.94209957.162309108.276608NaN49.351841NaN